膀胱癌是全球第10名最常見的癌症。由於對肌肉浸潤性膀胱癌(MIBC)和晚期膀胱癌(ABC)之間的致癌機制缺乏了解以及當前治療的局限性，迫切需要新的治療方法。在這項研究中，我們利用系統生物學方法通過全基因組微陣列數據探索MIBC和ABC的致癌機制，以辨別各自的藥物靶點進行系統藥物發現。首先，我們通過大數據挖掘構建候選全基因組遺傳和表觀遺傳網絡(GWGEN)。其次，我們通過它們的全基因組微陣列數據應用系統識別和系統順序檢測方法刪除候選GWGEN中的誤報，以獲得MIBC和ABC的真實GWGEN。第三，我們通過主網絡投影(PNP)方法選擇重要的蛋白質、基因和表觀遺傳，從真實的GWGEN中提取核心的GWGEN。最後，我們通過京都基因與基因組百科全書(KEGG)路徑的註釋從相應的核心GWGEN中獲得核心信號路徑，以研究MIBC和ABC的致癌機制。基於致癌機制，我們選擇MIBC的重要藥物靶點NFKB1、LEF1和MYC，以及ABC的LEF1、MYC、NOTCH1和FOXO1。為了設計MIBC和ABC的分子藥物組合，我們採用基於深度神經網絡(DNN)的藥物-靶點相互作用(DTI)模型和藥物規格。基於DNN的DTI模型由藥物-靶點相互作用數據庫訓練，分別預測MIBC和ABC的候選藥物。隨後，基於調節能力、敏感性和毒性的藥物設計規格作為篩選標準，從候選藥物中篩選出用於 MIBC的Embelin和Obatoclax以及用於ABC的Obatoclax、Entinostat和Imiquimod的潛在藥物組合。總而言之，我們不僅研究MIBC和ABC的致癌機制，而且分別為MIBC和ABC提供有希望的治療選擇。

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data for exploring the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic network (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGEN to obtain the real GWGENs of MIBC and ABC by their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed the deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combination of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigate the oncogenic mechanisms of MIBC and ABC but also provide promising therapeutic options for MIBC and ABC, respectively.

致謝----I
摘要----II
Abstract----III
Contents----V
Chapter 1 Introduction----1
Chapter 2 Methods and Materials----5
2.1 Overview of Systems Biology Methods and Systematic Drug Discovery and Design for MIBC and ABC----5
2.2 Data Preprocessing, Big Data Mining and the Construction of Candidate GWGEN----9
2.3 Systems Modeling for the Candidate GWGEN of MIBC and ABC----10
2.4 The System Identification Scheme and System Order Detection Method for Real GWGENs of MIBC and ABC----12
2.5 The Principal Network Projection (PNP) Method for Extracting the Core GWGENs from the Real GWGENs----18
2.6 Systematic Discovery and Design of Drug Combinations as Multiple-molecule Drugs for MIBC and ABC via Deep Neural Network----21
Chapter 3 Results----27
3.1 Overview of Systems Biology Approach for the Investigation of Carcinogenic Mechanism and Systematic Drug Design for the Treatment of MIBC and ABC----27
3.2 The Deep Neural Network-based Drug-Target Interaction Model with Drug Design Specifications to Discover the Potential Drug Combinations for Multiple-molecule Drugs of MIBC and ABC----34
Chapter 4 Discussion----42
4.1 The Specific Molecular Carcinogenic Mechanisms and the Investigation of the Drug Targets for MIBC----42
4.2 The Common Molecular Mechanisms and the Investigation of the Drug Targets between MIBC and ABC----43
4.3 The Specific Molecular Carcinogenic Mechanisms and the Investigation of the Drug Targets for ABC----45
4.4 The Potential Drug Combinations for the Drug Targets of MIBC and ABC----46
Chapter 5 Conclusion----48
Reference----50

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